{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from sklearn import svm\n",
    "import scipy.io\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pathname = 'hw01_data/'\n",
    "spam = scipy.io.loadmat(pathname + 'spam/spam_data_BOW_validation.mat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4138, 50501)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = spam['training_data']\n",
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.0, 1.0, 0.998, 0.998, 0.998, 0.999]\n",
      "[0.91102514506769827, 0.94003868471953578, 0.94680851063829785, 0.97292069632495159, 0.98259187620889743, 0.9874274661508704]\n"
     ]
    }
   ],
   "source": [
    "all_training_accuracy = []\n",
    "all_validation_accuracy = []\n",
    "all_training_size = [100, 200, 500, 1000, 2000, 4000]\n",
    "\n",
    "for size in all_training_size:\n",
    "\tidx = random.sample(range(0, spam['training_data'].shape[0]), size)\n",
    "\ttraining_data = spam['training_data'][idx]\n",
    "\ttraining_labels = spam['training_labels'].ravel()[idx]\n",
    "\tvalidation_data = spam['validation_data']\n",
    "\tvalidation_labels = spam['validation_labels'].ravel()\n",
    "\tclf = svm.LinearSVC(C=50)\n",
    "\tclf.fit(training_data, training_labels)\n",
    "\ttraining_accuracy = clf.score(training_data, training_labels)\n",
    "\tvalidation_accuracy = clf.score(validation_data, validation_labels)\n",
    "\tall_training_accuracy.append(training_accuracy)\n",
    "\tall_validation_accuracy.append(validation_accuracy)\n",
    "print(all_training_accuracy)\n",
    "print(all_validation_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4000 :  0.987427466151\n"
     ]
    },
    {
     "data": {
      "image/png": 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DfAZMcc59Ukb/64Ao4EfgL865fxfqcwMwATgO+BVYANzmnCu6ekxEqsShzEOsTlmdFzB+\n2PMDIUEh9O3Yl4d/9zCxnWLp0rxLsbd7rklZU2ygyA0ca1LWEHVGVJH9RKTu8ztsmNlw4AlgHLAO\nSABWmlkX59yvxfSfCEwDxgD/A84G/mFmu51zS319/gA8BPwRWAt0AeYAOXhDjYhUkU27NrH8O+/U\nyOqU1WRkZRAVEUVsp1gGdx5M/6j+NA5pXOZxSntgV1RElIKGSD1WkZGNBGCmc+4FADObAFwExAOP\nFtN/hK//At92ipmdBUwFlvraegEfOOde8W2nmtnLQM8K1CcipUjPTGd1yuq8gPHd7u8ICQrhvI7n\nMe38aQzuPJiuzbvqYVUiUmn8ChtmFgzEAH/JbXPOOTNbhTcwFKchkFGoLQPoaWZBzrls4EPgajM7\nyzn3iZmdAAwG5vpTn4gU7/vd3+dNjbyb8i4ZWRl0DO9IbKdYnrjgCc4//nyahDQJdJkiUkf5O7LR\nAggCdhRq3wF0LWGflcAYM1vonEs2sx7AaCDYd7wdzrlEM2sBfGDeX6eCgOecc4/4WZ+IABlZGaxJ\nWZMXMDbt3kSwJ5jzOp7Hg/0fJLZzLN1adNPohYhUi+q4G+UBoDWw1sw8wHa86zFuwbsmAzPrB9yO\nd4HoOqATMN3MtjnnHizt4AkJCYSHhxdoi4uLIy4urnKvQqSG+2HPD3nPvXh387ukZ6XToWkHYjvF\n8tjAxzj/+PM5puExgS5TRKpZYmIiiYmJBdrS0tKqtQZzzpW/s3ca5RAw1Dm3KF/7HCDcOXdZKfsG\n4Q0d24DxwMPOuQjfa+8BHznnbsnX/2q8az2KHds1s2ggKSkpiejo6HJfg0hdkZGVwXs/vpf33Itv\ndn1DA08Dzj3u3LzFnSe3PFmjFyJSRHJyMjExMQAxzrnkqj6fXyMbzrlMM0sCBgCLAHzTHgOA6WXs\nmw1s9e1zFbA438thQFahXXJHPcz5k4hE6rDNezbnLex8Z/M7HMo8xLFNjyW2UywPDXiIAScMoGnD\npoEuU0SkgIpMozwJzPGFjtxbX8PwTo1gZg8B7Zxzo3zbnfHeVfIxEAncCHQHrsl3zMVAgpl95uvX\nGbgfWKSgIfXZ4azDvJ/6ft7ai42/bqSBpwF9juvDPX3vIbZTLKe0OkWjFyJSo/kdNpxz832LOe/H\nOy2yHhjknNvp69IG6JBvlyDgJrzPzsgE3gV6O+dS8/V5AO9IxgNAe2An3pGTO/2tT6S2+3Hvj3mj\nF2//8DYHMw/S7ph2DO40mGnnT+N3J/xOoxciUqtUaIGoc24GMKOE164ttL0RKHVRhXMuN2g8UJF6\nRGqzI9lHeP/H9/MCxoadGwiyIP7vuP/jzvPuZHDnwZza6lSNXohIraXPRhEJgNS01LyFnW9vfpsD\nRw7QtklbYjvFcl+/+/jdCb8jolFEoMsUEakUChsi1eBI9hH+m/pfln+3nGWblvHVzq8IsiB6d+jN\n7X1uJ7ZzLKe3Pl2jFyJSJylsiFSRn/b9lDd6seqHVew/sp82TdoQ2ymWe/rew8ATB2r0QkTqBYUN\nkUqSmZ3Jf7f8Ny9gfPHLF3jMQ69jezH1/6YyuPNgTm9zOh7zBLpUEZFqpbAhchR+3vczK75bwbLv\nlrHqh1XsO7yP1o1bc2GnC7nj3DsYeOJAIkMjA12miEhAKWyI+CEzO5O1P63Neyz45zs+x2Mezjn2\nHP7c+8/EdorlzLZnavRCRCQfhQ2RMmzdv5UV361g+XfLefP7N9l3eB8tw1oS2zmW2/rcxgUnXqDR\nCxGRUihsiBSSlZPF2i1r8557sX77egzj7GPP5qZeNzG482Ci20Zr9EJEpJwUNkSAbfu35Y1evPXD\nW+zN2EuLsBZc2OlC/tz7z1xw4gW0CGsR6DJFRGolhQ2pl7Jysvj4p4/znnvx6fZPMYye7Xtyw9k3\nENs5lh7temj0QkSkEihsSL2x48COAmsv9mTsoXlocy7sdCE39bqJC068gJaNWwa6TBGROkdhQ+qs\n7JxsPv7547znXiRtS8IwerTrwZSeUxjceTA92vUgyBMU6FJFROo0hQ2pU345+Asrv1vJsu+W8eb3\nb7I7fTeRoZEMOnEQ1599PYM6DaJV41aBLlNEpF5R2JBaLTsnm0+2fpL33IukrUk4HD3a9eC6s64j\ntlMsPdv31OiFiEgAKWxIrbPz4E5Wfr+S5d8tZ+V3K9mVvotmjZoxqNMgpvScwqATB9G6SetAlyki\nIj4KGxIwc9fPpW9UX6Iiooq8lrI3hTUpaxh1xihyXA7/2/o/lm1axvLvlvPJz5/gcES3jWZij4nE\ndvaOXjTw6K+ziEhNpH+dJWD6RvUlfmE8sy+dXSBwpOxNYeTrIxnWbRgjXhvByu9X8uuhX4loFMEF\nJ17AxB4TubDThbRp0iZwxYuISLkpbEjAREVEMfvS2cQvjOf5S55n16FdvPTFS8z+dDb7juzjg9QP\nOLPNmYyLHkds51jOOfYcjV6IiNRC+pdbAioqIooZg2dw+nOnc+DIAYIsiEEnDmLYycO4sNOFtD2m\nbaBLFBGRo6SwIQE357M5ZGRlALByxEoGnDAgwBWJiEhl0rOYJaCStyXz2H8f47imx7F61GqmvT+N\nlL0pgS5LREQqkcKGBExmdiYjXhtBWHAYK0asoG9U37w1HAocIiJ1h8KGBMyd79zJ179+zUtDX6Jz\n885AwUWjChwiInVDhcKGmV1nZpvNLN3MPjKzs8rRf4OZHTKzr81sZKHX3zWznGK+FlekPqn5vt31\nLU+ufZJx0eO4uOvFBV7LDRxrUtYEqDoREalMfi8QNbPhwBPAOGAdkACsNLMuzrlfi+k/EZgGjAH+\nB5wN/MPMdjvnlvq6XQaE5NutBfAZMN/f+qTmy3E5jFk0ho4RHXnqwqeK7RMVEUXUGVHVW5iIiFSJ\nityNkgDMdM69AGBmE4CLgHjg0WL6j/D1X+DbTvGNhEwFlgI45/bm38HM/gAcBBYgdc6spFm8n/o+\n71zzDmHBYYEuR0REqphf0yhmFgzEAG/ntjnnHLAK6FXCbg2BjEJtGUBPMyvp07HigUTnXLo/9UnN\n99O+n7jlrVsYGz2W/sf3D3Q5IiJSDfxds9ECCAJ2FGrfAZT07OiVwBgziwYwsx7AaCDYd7wCzKwn\n0B143s/apIZzzjFhyQSOaXgMjw4sbhBMRETqoup4qNcDQGtgrZl5gO3AHOAWIKeY/qOBL5xzSdVQ\nm1Sjl798maWblvLG8DeIaBQR6HJERKSa+Bs2fgWy8YaH/FrjDRFFOOcy8I5sjPf12waMB/Y753bm\n72tmYcBw4M7yFpSQkEB4eHiBtri4OOLi4sp7CKkGvx76lT+t+BNXdr+SS0+6NNDliIjUG4mJiSQm\nJhZoS0tLq9YazLvkwo8dzD4CPnbOXe/bNiAVmO6ce6ycx1gNbHHOFb4F9o/ADKC9c25PGceIBpKS\nkpKIjo726xqk+o14bQTLNi3j6+u+pnWTwllVRESqU3JyMjExMQAxzrnkqj5fRaZRngTmmFkSv936\nGoZ3agQzewho55wb5dvuDPQEPgYigRvxrsm4pphjjwbeKCtoSO2yfNNyXvziReZcOkdBQ0SkHvI7\nbDjn5ptZC+B+vNMi64FB+aZE2gAd8u0SBNwEdAEygXeB3s651PzHNbMuQG9goL81Sc21//B+xi8Z\nzwUnXsA1pxeXL0VEpK6r0AJR59wMvNMdxb12baHtjUCZ8xzOuW/xBhOpQ257+zZ2p+9m5pCZeGfc\nRESkvtFHzEuV+SD1A2Z8MoO/XvhXoiKiAl2OiIgEiD6ITapERlYGYxaN4exjz+a6s64LdDkiIhJA\nGtmQKvHgew+yee9mXhv+GkEezY6JiNRnGtmQSvfZ9s945L+PcMe5d3Byy5MDXY6IiASYwoZUqqyc\nLEYvGs1JLU7i1j63BrocERGpATSNIpXqqbVP8en2T1k7ei0hQSGBLkdERGoAjWxIpflu93fcvfpu\nrj/7enq27xnockREpIZQ2JBK4Zxj7OKxtG3Slgf6PxDockREpAbRNIpUiueTn2d1ymreGvkWjUMa\nB7ocERGpQTSyIUft530/c/NbNxN/Rjy/O+F3gS5HRERqGIUNOSrOOa5bdh1hwWE8fsHjgS5HRERq\nIE2jyFFZsGEBC79ZyIIrFtAstFmgyxERkRpIIxtSYbsO7WLy8slc3u1yhp48NNDliIhIDaWwIRV2\n05s3cST7CM/EPhPoUkREpAbTNIpUyMrvVjL3s7n885J/0vaYtoEuR0REajCNbIjfDhw5wPgl4xlw\n/ACuPePaQJcjIiI1nEY2xG93vnMnvxz8hXdGvYOZBbocERGp4RQ2xC9rt6xl+sfTefyCxzmh2QmB\nLkdERGoBTaNIuR3OOsyYxWPo0a4H1599faDLERGRWkIjG1Juf3n/L3y761uSxyUT5AkKdDkiIlJL\naGRDyuWLHV/w0AcPcVuf2zi19amBLkdERGoRhQ0pU3ZONmMWj6FTZCfuOPeOQJcjIiK1jKZRpEzT\nP57OJz9/wn/j/0vDBg0DXY6IiNQyGtmQUv2w5wfufPdOpvScQq8OvQJdjoiI1EIVChtmdp2ZbTaz\ndDP7yMzOKkf/DWZ2yMy+NrORxfQJN7NnzWyrmWWY2UYzu7Ai9UnlcM4xfsl4Woa1ZNqAaYEuR0RE\naim/p1HMbDjwBDAOWAckACvNrItz7tdi+k8EpgFjgP8BZwP/MLPdzrmlvj7BwCpgO3A5sBXoCOyt\nyEVJ5Zizfg6rfljFiqtX0CSkSaDLERGRWqoiazYSgJnOuRcAzGwCcBEQDzxaTP8Rvv4LfNspvpGQ\nqcBSX9toIAI4xzmX7WtLrUBtUkm27d/GjW/eyDWnX8OgToMCXY6IiNRifk2j+EYgYoC3c9uccw7v\nqERJE/oNgYxCbRlATzPLfVjDxcBaYIaZbTezL8zsNjPTmpIAmbJ8CsGeYJ684MlAlyIiIrWcv2/m\nLYAgYEeh9h1AmxL2WQmMMbNoADPrgXckI9h3PIATgCt89cQC9wM3AbrPMgBe+/o1Xv36VZ4Z/AzN\nw5oHuhwREanlquPW1weA1sBa30jFdmAOcAuQ4+vjwRtYxvlGSj41s2OBm337lyghIYHw8PACbXFx\nccTFxVXmNdQbe9L3cN2y67ik6yVccfIVgS5HRESOUmJiIomJiQXa0tLSqrUG8763l7OzdxrlEDDU\nObcoX/scINw5d1kp+wbhDR3bgPHAw865CN9rq4EjzrkL8vW/EO+ajobOuaxijhcNJCUlJREdHV3u\na5DSjV44mgVfL2DDpA20b9o+0OWIiEgVSE5OJiYmBiDGOZdc1efzaxrFOZcJJAEDctvM+xnjA4AP\ny9g32zm31TdycRWwON/L/wU6FdqlK7CtuKAhVePtH95m9vrZPDbwMQUNERGpNBVZgPkkMNbMrjGz\nk4DngDC8UyOY2UNmNje3s5l1NrOrzayTmfU0s5eB7hRcj/F3INLMpvv6XwTcBjxTscsSfx08cpBx\nS8bRL6ofY6LHBLocERGpQ/xes+Gcm29mLfAu4mwNrAcGOed2+rq0ATrk2yUI72LPLkAm8C7Q2zmX\nmu+YP5nZIOAp4DPgZ9+fi7uVVqrA3e/ezdb9W1k5YiUe3QQkIiKVqEILRJ1zM4AZJbx2baHtjUCZ\niyqccx8DvStSjxyddT+v468f/5WHBjxEp8jCs1kiIiJHR7/C1nNHso8wetFozmhzBjf2ujHQ5YiI\nSB2kT32t5x754BG+3vk1/xv3Pxp49NdBREQqn0Y26rENOzfwwHsPMPX/pnJGmzMCXY6IiNRRChv1\nVHZONmMWjeGEZidwV9+7Al2OiIjUYRo3r6ee/eRZ1v60lvevfZ9GDRoFuhwREanDNLJRD/2490du\nf/t2JvWYRJ/j+gS6HBERqeMUNuoZ5xzjl4ynWWgzHvrdQ4EuR0RE6gFNo9Qz//7836z8fiVL4pbQ\ntGHTQJcjIiL1gEY26pEdB3aQsDKBP5z6By7qclGgyxERkXpCYaMe+dOKP+ExD38d9NdAlyIiIvWI\nplHqiYUbFzL/q/m8ePmLtGzcMtDliIhIPaKRjXogLSONScsmcVHni4g7JS7Q5YiISD2jsFEP3PLW\nLew/vJ9XVVSzAAAgAElEQVS/X/R3zCzQ5YiISD2jaZQ6bnXKamYlz+LZwc/SIbxDoMsREZF6SCMb\ndVh6ZjpjF4+lz3F9mNBjQqDLERGRekojG3XYvavvZUvaFpbELcFjypUiIhIYCht1VNLWJB5f+zgP\n9n+Qri26BrocERGpx/Trbh2UmZ3J6EWjOa31adzc++ZAlyMiIvWcRjbqoMc+fIwvf/mSdWPXERwU\nHOhyRESkntPIRh2z8deN3L/mfm7qdRPRbaMDXY6IiIjCRl2S43IYu3gsHcI7cG+/ewNdjoiICKBp\nlDrluf89xwepH/DuqHcJDQ4NdDkiIiKARjbqjNS0VKaumsq46HH0i+oX6HJERETyVChsmNl1ZrbZ\nzNLN7CMzO6sc/TeY2SEz+9rMRhZ6fZSZ5ZhZtu+/OWZ2qCK11UfOOSYunUjThk15dOCjgS5HRESk\nAL+nUcxsOPAEMA5YByQAK82si3Pu12L6TwSmAWOA/wFnA/8ws93OuaX5uqYBXYDcD+9w/tZWXyV+\nmciyTctYeNVCwhuFB7ocERGRAioyspEAzHTOveCc2whMAA4B8SX0H+Hrv8A5l+KcewWYBUwt1M85\n53Y6537xfe2sQG31zs6DO/nT8j8xvPtwLul6SaDLERERKcKvsGFmwUAM8HZum3POAauAXiXs1hDI\nKNSWAfQ0s6B8bU3MLMXMUs3sDTM72Z/a6qsbVt6AwzE9dnqgSxERESmWvyMbLYAgYEeh9h1AmxL2\nWQmMMbNoADPrAYwGgn3HA/gG78jIJcDVvro+NLN2ftZXryz9dikvffESTw16ilaNWwW6HBERkWJV\nx62vDwCtgbVm5gG2A3OAW4AcAOfcR8BHuTuY2Vrga2A8cE811Fjr7Du8jwlLJzDoxEGMPG1k2TuI\niIgEiL9h41cgG294yK813hBRhHMuA+/Ixnhfv214Q8T+ktZlOOeyzOxToFNZBSUkJBAeXnBRZFxc\nHHFxcWXtWqvduupW9qTvYeaQmZhZ2TuIiEi9lJiYSGJiYoG2tLS0aq3BvEsu/NjB7CPgY+fc9b5t\nA1KB6c65x8p5jNXAFudcsb+S+0ZAvgKWOueK/SQx37RMUlJSEtHR9eux3O//+D7nzTmP6RdOZ8rZ\nUwJdjoiI1DLJycnExMQAxDjnkqv6fBWZRnkSmGNmSfx262sY3qkRzOwhoJ1zbpRvuzPQE/gYiARu\nBLoD1+Qe0MzuwjuN8h0QgXeK5Tjg+YpcVF2WkZXBmMVj6HVsLyadNSnQ5YiIiJTJ77DhnJtvZi2A\n+/FOi6wHBuWbEmkDdMi3SxBwE95naGQC7wK9nXOp+fo0w3s7bBtgD5AE9PLdWiv53L/mflL2pvD6\n8NcJ8gSVvYOIiEiAVWiBqHNuBjCjhNeuLbS9ESh1nsM5dyPeEQ8pxfrt63n0v49yT997OLml7gwW\nEZHaQZ+NUktk5WQxetFoTm55MlP7FH4emoiISM2lT32tJZ5c+yTrt6/no9EfERIUEuhyREREyk0j\nGzXQ3PVzSdmbkre9adcm7ll9DzecfQMtG7dk7vq5gStORETETwobNVDfqL7EL4wnZW8KOS6HsYvH\n0u6Ydow+czTxC+PpG9U30CWKiIiUm6ZRaqCoiChmXzqb+IXxDDh+AGt+XMO8y+YxeflkZl86m6iI\nqECXKCIiUm4KGzVUx/COXHDiBdzxzh0M7jSYf376TwUNERGplRQ2aqAtaVsYu3gsK79fSWynWJZ9\nt4zVo1YraIiISK2kNRs1iHOOfyb/k1P+fgpf/PIFsy+ZTUZWBqtHrea+NfcVWDQqIiJSWyhs1BA/\n7fuJwS8NZsziMVze7XKWX72cf3/+b2ZfOpu+UX3z1nAocIiISG2jsBFgzjn+9em/6D6jO5/v+Jwl\ncUu4p+893LDihgJrNPIvGlXgEBGR2kRhI4B+3vczQxKHEL8onstOuowvJ37JRV0uYk3KmmIXg+YG\njjUpawJTsIiISAVogWgAOOeY+9lcblhxA2HBYSyOW8yQLkPyXh91xqgS942KiCLqjKhqqFJERKRy\nKGxUs5/3/cz4JeNZumkpI08bydMXPk2z0GaBLktERKTKKGxUE+ccL3z2AjesvIFGDRqx6KpFXNz1\n4kCXJSIiUuUUNqrB1v1bGb9kPEu+XcKI00bw9IVPExkaGeiyREREqoXCRhVyzjHv83n8acWfaNSg\nEQuvWsglXS8JdFkiIiLVSmGjimzbv40JSyew6JtFXH3q1UyPna7RDBERqZcUNiqZc46XvniJKcun\nEBIUwuvDX+f3J/0+0GWJiIgEjMJGJdp+YDsTlkxg4TcLiTsljr/F/o3mYc0DXZaIiEhAKWxUAucc\niV8mMmX5FBp4GvDqla9yebfLA12WiIhIjaCwcZR2HNjBhKUTeGPjGwzvPpxnBj9Di7AWgS5LRESk\nxlDYqCDnHK989QqTl03GYx4WXLGAoScPDXRZIiIiNY7CRgX8cvAXJi6dyGtfv8aV3a/kmdhnaNm4\nZaDLEhERqZEUNvw0/6v5TFo6CTPjP1f8h2EnDwt0SSIiIjVahT711cyuM7PNZpZuZh+Z2Vnl6L/B\nzA6Z2ddmNrKUvleZWY6ZvVaR2qrKLwd/4Yr/XMHwBcM5//jz2TBpg4KGiIhIOfgdNsxsOPAEcA9w\nJvAZsNLMil0VaWYTgWnA3cDJwL3As2Z2UTF9o4DHgPf8retozV0/l5S9KcW+9uy6Zzlx+omsTlnN\nK8NeYf4V8zVtIiIiUk4VGdlIAGY6515wzm0EJgCHgPgS+o/w9V/gnEtxzr0CzAKm5u9kZh5gHt5Q\nsrkCdR2VvlF9iV8YXyBw7Dy4k4tevIjJyyfTp0Mfvpr0FVd2v7K6SxMREanV/AobZhYMxABv57Y5\n5xywCuhVwm4NgYxCbRlATzMLytd2D7DDOfcvf2qqLFERUcy+dHZe4Hh1w6uc9MxJvPnDm0y/cDrL\nrl5Gq8atAlGaiIhIrebvAtEWQBCwo1D7DqBrCfusBMaY2ULnXLKZ9QBGA8G+4+0wsz7AtcDpftZT\nqXIDx7mzz+Wn/T/RMqwlH8Z/yFntS12SIiIiIqWojrtRHgBaA2t9UyXbgTnALUCOmTUBXgDGOuf2\n+HvwhIQEwsPDC7TFxcURFxdXoWI7hndk56GdAMwfNl9BQ0REarXExEQSExMLtKWlpVVrDeadBSln\nZ+80yiFgqHNuUb72OUC4c+6yUvYNwhs6tgHjgYedcxFmdjqQDGQD5uueO72TDXR1zhVZw2Fm0UBS\nUlIS0dHR5b6Gsqzfvp4zZ57Jff3uY3XKamZfOpuoiKhKO76IiEigJScnExMTAxDjnEuu6vP5tWbD\nOZcJJAEDctvMzHzbH5axb7ZzbqtvjcdVwGLfSxuBU4Ez8E6jnA4sAt7x/XmLPzUejZS9KYxbPA6A\n2E6xBdZwiIiISMVU5G6UJ4GxZnaNmZ0EPAeE4Z0awcweMrO5uZ3NrLOZXW1mncysp5m9DHQH7gBw\nzh12zm3I/wXsBfY75752zmUd3SWWT8reFOIXxjM2eiwAHcI7FFk0KiIiIv7zO2w45+YDNwP3A58C\npwGDnHM7fV3aAB3y7RIE3ASsx7tYNATo7ZxLPYq6K92alDXMvnQ2h7MPExIUknfnSW7gWJOyJsAV\nioiI1E4VWiDqnJsBzCjhtWsLbW8E/FpUUfgY1WHUGaMA2JK2hQ5NO+Cx33JYVEQUUWdEVXdJIiIi\ndUKFHldel6XuS+W48OMCXYaIiEidobBRSGqawoaIiEhlUtgoJDUtlQ5NO5TdUURERMpFYSOfrJws\ntu7fqpENERGRSqSwkc/W/VvJcTkKGyIiIpVIYSOf1DTv3bgKGyIiIpVHYSOf3LDRIVxrNkRERCqL\nwkY+qWmpNGvUjCYhTQJdioiISJ2hsJHPlrQtmkIRERGpZAob+eiBXiIiIpVPYSMfPdBLRESk8ils\n5KMHeomIiFQ+hQ2f/Yf3szdjr0Y2REREKpnChs+WfVsAPWNDRESksils+OiBXiIiIlVDYcMnNS0V\nj3loe0zbQJciIiJSpyhs+KSmpdL+mPY08DQIdCkiIiJ1isKGj257FRERqRoKGz5b9unpoSIiIlVB\nYcNHIxsiIiJVQ2EDyHE5bEnbogd6iYiIVAGFDWDHgR1k5mRqZENERKQKKGygB3qJiIhUpQqFDTO7\nzsw2m1m6mX1kZmeVo/8GMztkZl+b2chCr19mZp+Y2R4zO2Bmn5rZiIrUVhF6oJeIiEjV8fuhEmY2\nHHgCGAesAxKAlWbWxTn3azH9JwLTgDHA/4CzgX+Y2W7n3FJft13Ag8BG4AhwMfAvM9vhnHvL/8vy\nT2paKo2DGxPRKKKqTyUiIlLvVGRkIwGY6Zx7wTm3EZgAHALiS+g/wtd/gXMuxTn3CjALmJrbwTn3\nnnNuoXPuG+fcZufcdOBzoE8F6vNb7p0oZlYdpxMREalX/AobZhYMxABv57Y55xywCuhVwm4NgYxC\nbRlATzMLKuE8A4AuwBp/6qso3fYqIiJSdfwd2WgBBAE7CrXvANqUsM9KYIyZRQOYWQ9gNBDsOx6+\n9qZmtt/MjgCLgSnOuXf8rK9C9EAvERGRqlMdd6M8ACwH1ppZJvA6MMf3Wk6+fvuB04EewB3AU2Z2\nXjXUp5ENERGRKuTvAtFfgWygdaH21sD24nZwzmXgHdkY7+u3DRgP7HfO7czXzwE/+DY/N7OTgduA\n90orKCEhgfDw8AJtcXFxxMXFleuC0jPT+eXgL3qgl4iI1EmJiYkkJiYWaEtLS6vWGvwKG865TDNL\nAgYAiwDMu6pyADC9jH2zga2+fa7CO1VSGg/e9R6leuqpp4iOji67+BL8tO8nQLe9iohI3VTcL+DJ\nycnExMRUWw0V+Tz1J4E5vtCRe+trGL6pETN7CGjnnBvl2+4M9AQ+BiKBG4HuwDW5BzSzW/HeFvs9\n3oBxEd67WCZU5KL8oWdsiIiIVC2/w4Zzbr6ZtQDuxzstsh4YlG9KpA2Qf04iCLgJ790lmcC7QG/n\nXGq+Po2BZ4FjgXS8z9u42jm3wN/6/JX79NBjmx5b1acSERGplyoysoFzbgYwo4TXri20vREodZ7D\nOXcXcFdFajlaqWmptG7cmoYNypyxERERkQqo95+NojtRREREqpbChsKGiIhIlar3YUMP9BIREala\n9TpsOOc0siEiIlLF6nXY2J2+m0OZh/RALxERkSpUr8OGnrEhUn998803eDwe5s+f7/e+hw8fxuPx\n8Oijj1ZBZRWzcuVKPB4P69atC3QpIkUobKCwIVITeDyeMr+CgoJ4771SP8HAL94HIFd836PZvypU\ntJ7Fixczbdq0Sq5G5DcVes5GXbFl3xYaBjWkZeOWgS5FpN6bN29ege25c+eyatUq5s2bh/ejk7y6\ndetWKefr2rUr6enphISE+L1vw4YNSU9PJzg4uFJqCbRFixbx4osvcscddwS6FKmj6nXYSE1L5dim\nx+Kxej3AI3XM3LnQty9ERRV9LSUF1qyBUaNq3rH/8Ic/FNheu3Ytq1atKveHKmZkZNCoUSO/zlmR\noFEZ+9Y0+cNcfVaRv0NSPvX6XVZ3okhd1LcvxMd73/zzS0nxtvftWzOP7Y/c9Qmvv/46U6dOpX37\n9jRp0oQjR47w66+/kpCQwCmnnEKTJk2IiIjg4osvZsOGDQWOUdyajauuuoqWLVuyZcsWhgwZwjHH\nHEPr1q2L/MZf3JqNW2+9FY/Hw5YtWxgxYgQRERFERkYyfvx4jhw5UmD/Q4cOMWnSJJo3b07Tpk0Z\nNmwYP/74Y7nXgfz4449cfPHFNGnShDZt2nDLLbeQmZlZpN+7777LsGHDOO6442jUqBFRUVFMnTq1\nQD1xcXHMnj0775o8Hg9hYWF5rz/00EP07t2b5s2bExYWxtlnn82iRYvKrLG858/11VdfMXToUFq2\nbElYWBgnn3wy9913X4E+W7Zs4Y9//CNt27YlNDSUTp06MWXKlLywdOuttxIaGlrk2M899xwej4df\nfvklr61NmzZceeWVLF26lJiYGBo1asQLL7wAwD/+8Q/OP/98WrduTWhoKKeeeiqzZ88u9hoXL17M\neeedxzHHHENERATnnHMOr776aoF69u3bV2S/a665hlatWpGdnV2u72VtV+9HNro07xLoMkQqVVQU\nzJ7tffOfPdu7nRsGcrdr4rEr4q677qJx48ZMnTqVgwcPEhQUxDfffMOKFSsYNmwYHTt2ZNu2bTz3\n3HP069ePDRs20KJFixKPZ2ZkZmYycOBA+vXrx+OPP86KFSt4+OGH6dKlC6NKGbbJXcPx+9//ni5d\nuvDII4+wbt06nn/+edq1a8c999yT1zcuLo4lS5YQHx9PTEwMq1at4ve//3251lwcOHCA/v37s3Pn\nThISEmjRogVz587lzTffLNL3lVdeISsri8mTJ9OsWTM++ugjnnjiCbZv387cuXMBmDJlCjt27ODD\nDz/kX//6F845goKC8o7x9NNPM3z4cK655hoOHz7MvHnzuPzyy3nzzTc5//zzS621POcHSEpKol+/\nfjRu3JhJkybRoUMHNm3axNKlS/O+b1u2bOGss84iPT2d8ePH06VLF1JTU5k/fz6ZmZmEhISUuI6m\nuHYz4/PPP2fUqFFMmjSJCRMm0L17dwBmzJjBWWedxWWXXYbH4+GNN95gzJgxmBnXXvvbJ3I899xz\nTJo0iTPPPJM777yTpk2bkpyczMqVKxk6dCgjR47kscceY8GCBcTHx+ftl56ezhtvvMEf//jHAt/r\nOs05Vyu/8H7eiktKSnIV1f6J9u6ud+6q8P4iNdnmzc6dd55zs2Y516OHc4sXO5eUVDlfixd7jzlr\nlvccmzdXfv2TJ092Ho+n2NdWrFjhzMydfPLJLjMzs8Brhw8fLtJ/06ZNLiQkxD3++ON5bRs3bnRm\n5l555ZW8tquuusp5PB73xBNPFNi/e/fu7txzz83bzsjIcGbmHnnkkby2W2+91ZmZmzJlSoF9Bw8e\n7Dp06JC3/eGHHzozc3fccUeBfnFxcc7j8RQ4ZnEefvhh5/F43NKlS/PaDh486KKiopzH43Eff/xx\ngToLu/fee12DBg3cL7/8ktc2ZswYFxoaWuz5Ch/jyJEjrmvXrm7IkCGl1unP+Xv27OmaN2/utm/f\nXuKxrrzyShcSEuK+/PLLEvvceuutxV7Hc8895zwej9uxY0deW5s2bZzH43Hvv/9+ueru37+/O+WU\nU/K2d+3a5cLCwly/fv2K/B3MLzo62vXv379A20svveQ8Ho9bt25diftVtaSkJAc4INpVw3t2vR3Z\nyMzOZNuBbZpGkTorKgpGjIBx47zbF19c+ef43/9g1qzqH9HIFR8fT4MGBf8Zy7+WIjs7m7S0NCIi\nIjj++ONJTk4u13HH5X7TfPr06cOSJUvK3M/MGD9+fIG2c889l5UrV5KZmUlwcDArVqzAzJg4cWKB\nflOmTOHll18u8xzLly8nKiqKwYMH57WFhYUxevToAqMn4F3ImuvQoUOkp6fTu3dvcnJyWL9+PQMH\nDizzfPmPsXfvXrKysvi///s/VqxY4de+JZ3/559/5pNPPuG2226jdevWxR4nKyuLJUuWMGzYsLzR\nh8rQrVs3+vTpU2rdaWlpZGZmct555/Hggw9y5MgRQkJCWL58ORkZGdx+++1F/g7md80113DTTTfx\n888/0759ewBefPFFOnXqxFlnnVVp11LT1duwsXX/VnJcjh7oJXVWSgrMm+cNA7NmwT33QLt2lXPs\nrVvhvvu8QWbePBg4MDCBI6qYk+bk5PD4448zc+ZMfvzxR3JycgBvEOjUqVOZx4yIiKBJkyYF2po1\na8aePXvKVdNxxxX8BaZZs2Y459i7dy8tW7bkxx9/pGHDhnlvPLnKUxt412t07dq1SHtxbSkpKdx5\n550sW7aMvXv35rWbGWlpaeU63+uvv85DDz3EF198weHDh/Pa86/rKEl5zv/9998DlBoitm7dSnp6\neqUGDYDjjz++2PY1a9Zw7733sm7dOtLT0/PazYx9+/bRokWLctUN3oXPf/7zn0lMTOTmm29m165d\nvPnmm9x9992VdyG1QL0NG3rGhtRlueso5s71hoCBAytvXUVKCtx8M/znP5V/bH8Vtxjw7rvv5i9/\n+QsTJkygf//+NGvWDI/Hw8SJE/OCR2lKmkN35bxj42j3ryxZWVmcf/75ZGRkcOedd9KlSxfCwsJI\nSUlh7Nix5fpevPXWWwwdOpSBAwcyc+ZM2rRpQ4MGDXjuuefKHOkp7fxjxowp1/n9VdKal5IWYRb3\n92fjxo1ccMEFnH766Tz99NMce+yxhISE8MYbb/Dss8/6XXfLli0ZNGgQ8+bN4+abb+bll18mOzub\nq6++2q/j1Hb1Pmx0CNfIhtQtxS3YLG5hZ007dmV59dVXGTx4MDNmzCjQvnv3bk488cQAVfWbjh07\ncvjw4QLD6gCbNm0q9/7F9d24cWOB7aSkJFJSUvjPf/7D0KFD89qXLFlSJPiU9Cb92muvER4ezvLl\ny/F4frt58dlnny2zztLOn1/uz+TLL78s8Vjt2rUjNDS01D7gHUU6fPhw3lRHrpTCt0+VYuHChWRl\nZbFs2bICi4mXLl1aYt3tyhgyvOaaa7jqqqv48ssveemll+jVq1eJoyp1Vb299XXLvi1EhkbSJKRJ\n2Z1FapE1a4p/088NBWvW1Mxj+6ukN8igoKAib6b//ve/2bVrV3WUVaZBgwbhnCsShv72t7+V626U\nwYMHk5KSUuDN78CBA0VuzcwdYcn/m7hzjqeffrrIeRo3bszhw4cLTJPkHsPj8RQYGdi0aRPLli0r\ns87ynr99+/b07NmTWbNmsW3btmKP1aBBAy6++GJeffXVUgPHiSeeiHOuwFNm9+3bx4svvlhmvaXV\nvWvXriIPnYuNjaVRo0b85S9/Kfa24/wuueQSmjZtyv3338/atWsZOXJkueupK+r1yIbWa0hdVNpD\ntaKijm7koSqP7a+SpiWGDBnCY489xrhx4zjrrLP47LPPeOWVV4pd3xEIvXv35qKLLuLhhx9m+/bt\n9OjRg7fffpvNmzcDZT9yfNKkSfz9739n+PDhXH/99bRq1Yo5c+YQERFBampqXr9TTz2V4447jilT\npvDDDz/QuHFj5s+fz4EDB4ocMyYmBoDrrruO888/n5CQEIYNG8aQIUOYMWMGF154IcOHD2fr1q3M\nmDGDk046iW+++abUOv05/zPPPEP//v0588wzGTt2LB07duT777/nnXfe4eOPPwbgkUceYfXq1fTu\n3Zvx48fTtWtXfvrpJ+bPn8/69esJCQlhyJAhtGnThpEjR3LzzTfjnOOf//wn7du3Z/v27aX/YHwu\nvPBCbr/9dmJjYxkzZgx79+5l1qxZtG/fnl9//TWvX2RkJI8//jiTJ0/m7LPPZvjw4YSHh7N+/Xqc\nc8ycOTOvb8OGDbniiit4/vnnCQkJ4corryxXLXVKddzyUhVfHOWtrxe9eJG7+KWLK7SviFS9yZMn\nu6CgoGJfW7FiRZHbP3Olp6e7G264wbVr1841adLE9e/f3yUnJ7tevXq5wYMH5/XbuHGj83g8RW59\nbdWqVZFj3nrrrS4sLCxvOyMjw3k8Hvfoo48W6BMUFOQOHjxYYN/ibrs8ePCgmzhxoouMjHRNmzZ1\nw4YNc1999ZUzMzd9+vQyvzcpKSluyJAhrnHjxq5NmzZu6tSpbsmSJUVuff3yyy/dgAED3DHHHONa\nt27tJk+e7JKSkopcd1ZWlps0aZJr1aqVCwoKKnD76KxZs1znzp1daGioO+WUU9xLL71U4i2mhZX3\n/M459/nnn7vf//73LjIy0jVu3Nh1797dTZs2rch1jxw50rVq1cqFhoa6zp07u4SEBJeTk5PXZ926\nda5nz56uUaNG7oQTTnAzZswo9mfQtm1bd+WVVxZb9xtvvOFOPfVUFxoa6jp16uSefvrpYo/hnHOv\nv/666927t2vcuLGLiIhwvXv3dq+99lqRY77//vvOzNxll11W5vetOlT3ra/mqnnRUmUxs2ggKSkp\niejoaL/3P+3vp3Fex/N4ZvAzlV+ciIifPvroI3r37s2rr77KZZddFuhypJKtW7eOc845hwULFnD5\n5ZcHuhySk5NzR7RinHPluyf8KNTbNRt6VLmIBEpGRkaRtqeffprg4OBin/sgtd+sWbNo1qwZQ4YM\nCXQpAVEv12zsO7yPtMNpChsiEhAPPPAAGzdu5LzzzsPMWLJkCW+//TbXX389LVvqU6jrkkWLFvHl\nl18yd+5cbr311jr1AX7+qJdhY0vaFgAtEBWRgOjTpw+rV6/m/vvv5+DBg3Ts2JFp06YxderUQJcm\nlWz8+PHs37+foUOHcvvttwe6nICpl2FDD/QSkUCKjY0lNjY20GVINSjpdt76pkJrNszsOjPbbGbp\nZvaRmZX6gHdf/w1mdsjMvjazkYVeH2Nm75nZbt/XW2Ud82ikpqUSZEG0PaZtVZ1CREREfPwOG2Y2\nHHgCuAc4E/gMWGlmxX5us5lNBKYBdwMnA/cCz5rZRfm69QVeAvoB5wBbgDfNrErSwJZ9W2jftD0N\nPPVyYEdERKRaVWRkIwGY6Zx7wTm3EZgAHALiS+g/wtd/gXMuxTn3CjALyJucdM6NdM4955z73Dn3\nLTDGV9uACtRXJj3QS0REpPr4FTbMLBiIAd7ObXPeB3WsAnqVsFtDoPB9XhlATzMr/hOLoDEQDOz2\np77y0m2vIiIi1cffkY0WwP+3d+5hVVVbw//NTchVRcsgROWAt6w83k3FtMw0zExKU/GSmNn58rwd\ns5N2JAnzUp0nOWqat0oT4UvzRlnqW1bmpUwsv6y8HAovkFoIiokGOL4/1t60N3tz2chls5m/51nP\n45prrDnHWGPhGnvOMef0AM4WKz8LBJVwz3bgcfMiXCilugATMIIJh0MvwCtABkYQU+noYEOj0Wg0\nmitMGzIAABugSURBVOqjOpIWXgICgX1KKRNwBlgFPAfY7dWrlJoODAf6iMgfZVU+ZcoUGjZsaFM2\ncuRIRo4c6VD+mlzj9MXTOtjQaDQaTZ0gOTmZ5ORkm7ILFy5Uqw7OBhu/AYUYwYM1gRhBhB0icgWj\nZ2OSWe4XYBKQKyK/WssqpZ7FCEL6icj35VEoISHBqeXKz146S/61fB1saDQajaZO4OgHuNVy5dWC\nU8MoIpIPpGKVuKmMLQr7AXvLuLdQRDLNOR4jgPetryulngNmAANE5Btn9HIGyxobOkFUo6k7hISE\n8MQTTxSdf/LJJ5hMJvbuLfW/LcBYgOu+++6rVH1iY2Px9PSs1Dqvh7S0NEwmE0lJSTWtisZNqchs\nlPnARKXUWKVUW2Ap4IsxNIJSap5SarVFWCnVSikVrZRqqZTqppT6v8BtGIGFRWYaMAtjRstJpVSg\n+fCrsGUloBf00mhckyFDhuDn58fvv/9eokx0dDReXl5kZ2c7VbejbdvL2srdWbni/P7778THx7N7\n926HdZpM7rE11Z49e4iPj3e4dbxGY8Hpt11E1gHPYgQH3wDtMXojLEMiQYB1t4EHMBX4FiNZtB7Q\nU0ROWsk8iZEw+h6QaXVMdVa/sjh54ST+9fwJ8A6o7Ko1Gs11EB0dzZUrV9i0aZPD63l5eaSkpBAZ\nGUmjRo2uq61+/fqRl5dHz549r6ue0rh06RLx8fHs2rXL7po7fZx3797NrFmzuHjxYk2ronFhKpQg\nKiJLgCUlXBtf7PwIUGpShYj8pSJ6VATLTJSK/lrRaDRVw4MPPoi/vz9JSUmMHj3a7vrmzZu5fPky\n0dHRldJeVW+IZYwYO8ZkMrlNz0ZpdtYl8vLy8PHxqWk1XBb3eNud4NTFUzpfQ+PWrP52Nek56Q6v\npeeks/rb1Q6v1XTd3t7eREVF8cknn/Dbb7/ZXU9KSqJ+/foMHjy4qOyVV16hV69e3Hjjjfj6+tK1\na1c2b95cZlsl5Wy88cYbhIeH4+vrS48ePRzmdFy9epUXXniBzp07ExAQgL+/P3379uWLL74okklL\nSyM4OBilFLGxsUXBxdy5cwHHORsFBQXEx8cTHh6Ot7c3YWFhzJw5k/z8fBu5kJAQoqKi2LVrF926\ndcPHx4eWLVuWO98iOzubsWPHEhAQQOPGjZkwYYLDXolDhw4xbtw4wsLC8Pb25pZbbmHixIk2Q1gv\nvPBC0eZiISEhmEwmPDw8yMzMBODNN9+kX79+BAYG4uPjw+23386KFSvKpWd52rdw+vRpYmJiCA4O\nxsfHh/DwcCZPnsy1a39OeMzOzubpp58mNDQUb29vmjdvzmOPPUZOTg4AK1euxGQyFeluwdG7EhER\nQadOnfj666/p3bs3fn5+xMXFAbBp0yYGDRpE06ZN8fb2plWrVsydO9dhULZv3z7uv/9+GjVqhL+/\nPx06dGDx4sU2+nz/vf1ciVmzZuHp6cm5c+fK9SxdgToXbOg1NjTuTp/QPsRsibELCtJz0onZEkOf\n0D4uWTcYQyn5+fmsW7fOpjw7O5sdO3YQFRWFl5dXUfnChQvp3Lkzs2fPZt68eZhMJh5++GF27NhR\nZlvFezeXLVvGU089RbNmzfj3v/9Njx49GDx4sN3HJycnh1WrVtGvXz9effVVXnzxRc6cOcN9991X\n9GEICgpi8eLFiAjDhg0jMTGRxMREHnrooaK2i7f/2GOPER8fT/fu3UlISKB3797Mnj3brpdHKcXR\no0cZMWIEAwcOZP78+TRs2JBx48Zx/PjxUm0WEQYPHkxycjLjxo1j9uzZpKenM378eDt9tm/fzqlT\np5gwYQKLFy9mxIgRrF271ibYGz58OI8++igAr7/+OomJiaxZs4bGjRsDRvAWFhbGjBkzeO2112ja\ntCmTJk0qV8BRnvYBMjIy6Nq1K+vXryc6OppFixYxevRodu7cyZUrxnqSly5dIiIigqVLlxIZGcnC\nhQuZNGkSP/zwQ5F/HfnE+pkXPz937hwPPPAAXbt2ZcGCBfTpY7z7q1atomHDhkydOpUFCxbQsWNH\nYmNjiY2Ntalj27Zt9O3bl2PHjjF16lTmz59P37592bp1KwDDhg3D29ubtWvX2umTlJRE//79ufnm\nm8t8ji6DiNTKA2NoRlJTU8UZmrzaRF76/CWn7tFoahs/Z/8sd6+6W37O/tnhuavWXVhYKMHBwdKr\nVy+b8qVLl4rJZJKPP/7YpvzKlSs25/n5+dKuXTsZOHCgTXlISIhMnDix6Pzjjz8Wk8kke/bsERGR\nP/74Q2666Sbp1q2bFBQU2LSrlJL+/fvb6Jifn29Tf05OjjRp0kSefPLJorIzZ86IUkrmzJljZ2ds\nbKx4enoWnaempopSSp566ikbuSlTpojJZJLdu3fb2GIymeTLL7+0aatevXry/PPP27VlzXvvvSdK\nKVmwYIGNPb169RKTySRr164tKi/+bEVEEhMT7dp++eWXxWQySUZGhp28ozruvfdeadu2bal6OtP+\nqFGjxNPTUw4dOlRiXf/617/EZDLJ1q1bS5RZuXKlQzuKvysiIhEREWIymeTtt98ul96PP/64NGjQ\noOjdKigokObNm0urVq0kNze3RJ2GDx8uLVq0sCnbv3+/KKUkKSmpxPvKQ2pqqgACdJJq+GbXqZ6N\nvPw8fr38q+7Z0Lg9oQGhvDXkLcZtHseK1BUMWz+MZ3o8w/m88xz85eB1HefzzvNMj2cYtn4YK1JX\nMG7zON4a8hahAaHXrbfJZGLEiBHs27ePkyf/zCFPSkoiMDCQe+65x0beupcjJyeHnJwcIiIiOHjw\noFPtfvXVV2RlZfG3v/0ND48/d1GIiYmhfv36djrecIOR7iYiZGdnk5+fT5cuXZxu18KHH36IUoop\nU6bYlE+dOhURKfq1a6F9+/Z079696DwwMJBWrVrx008/ldrORx99hJeXl800YJPJxOTJk+26+a2f\n7dWrV8nKyqJ79+6ISLnttK7j4sWLZGVl0adPH44dO0ZeXl657y2p/cLCQlJSUhg6dCjt27cvsa6N\nGzfSuXNnIiMjy6V3efD19WXMmDF25dZ6X7p0iaysLCIiIrh06RLHjh0D4MCBA5w6dYopU6bg7+9f\nYhtjx47l1KlTNkN0a9euxd/fv6iXrLZQp7Y9PX3xNKCnvWrqBqEBoYy+YzRPfGB8WAYnDy7jDuc5\nkHmA5Q8sr5RAw0J0dDQJCQkkJSUxffp0MjIy2L17N//4xz/surNTUlKYO3cuhw4d4urVq0XlziZ/\nnjhxAqUULVu2tCn39PQkNDTUTv7tt99m/vz5HD16lIKCgqLy1q1bO9Wudfs33HAD4eHhNuVNmzal\nfv36nDhxwqa8eXP7/8MaNWpU5pTgEydOFOUSWNOmTRs72aysLF588UXWrVvHr7/+uf6iUqrcq09+\n8cUXxMXFsX//fi5fvmxXR2kJleVp/+zZs/z+++/cdtttpeqRlpbmMOn4eggJCbEJTC0cPnyYGTNm\n8Nlnn5Gbm+tQ77S0NJRSZeo9cOBAmjRpwtq1a+nduzfXrl3j3XffJSoqqtYlo9apYEMv6KWpS6Tn\npJP4XSLLH1jO8oPLiesTR3D94EqpOzM3k/jP43mi0xMkfpdI//D+lRZwdOrUibZt25KcnMz06dOL\nEh9HjRplI/fpp58ydOhQ7rnnHpYuXUpQUBCenp6sWLGCDRs2VIoujli1ahUTJkzgkUce4fnnn6dJ\nkyZ4eHjw0ksvkZGRUWXtWuPoIweVOzPk4YcfJjU1lWnTptG+fXv8/PzIz88nMjLSJvGyJI4fP07/\n/v25/fbbSUhIoFmzZtSrV4+UlBQWLVpUZh3X276zlJSvUVhY6LDc0cc+Ozubu+66ixtvvJF58+YV\nJaPu37+fGTNmOK23h4cHI0eOZM2aNSxatIidO3dy7ty5Sg+cqoM6GWyENAipYU00mqrFkrC5+qHV\nhAaE0j+8PzFbYipluCM9J51ndzzL+mHrK71uC9HR0cycOZPvvvuO5ORkWrVqZbe08saNG/Hz82Pb\ntm02H99ly5Y53V6LFi0QEY4fP05ERERReX5+Punp6QQG/rlDw4YNG2jTpo1dEqtlVoYFZ6bXt2jR\ngoKCAtLS0mx6NzIzM8nNzaVFixbOmlRiO7t37+bKlSs2vRtHjhyxkcvKymLXrl3MmzePadOmlSgH\nJduZkpJCfn4+W7dutXl+27dvL1PP8rYfGBiIn58fhw8fLrW+8PDwMmUsa7fk5OQQHPxnUJ6enl6m\nvhZ27tzJhQsX+Oijj2yGuY4ePWqnj4hw+PBh7rrrrlLrHDt2LAsXLuTDDz9k48aNBAUF0a9fv1Lv\ncUXqVM7GyQsnCfIPwusGr7KFNZpaiiXQsP74W3I4HM0kcZW6rYmOjkZEmDlzJt9++63DX3IeHh6Y\nTCabX54//fQT77//vp1sWXTv3p3GjRuzdOlSm/pWrlxp0xVuabc4e/bs4euvv7Yp8/MzFkC2TK0s\njcjISESE//znPzblr732GkopBg0aVG5bymrn6tWrNgFZYWEhr7/+uk3QYLGx+C/xhIQEu+CiJDsd\n1ZGdnc0777xTpp7lbd/Dw4MhQ4awefNmDh06VGJ9ll6S4rkv1lgCAOtF2AoLC1m+fHmZ+pam99Wr\nV3njjTds5Lp27Urz5s1JSEgoczG0jh070q5dO5YvX86mTZsYNWpUrVwnqs71bOh8DY2783n65w57\nGSxBwefpnxPaIdThvTVZt019oaH07NmTLVu2oJSyG0IBGDRoEAsXLmTAgAGMHDmSX375hSVLltCm\nTRuHaxMUx3rIwdPTk5deeonJkydz99138+ijj/Lf//6Xd955h7CwMJv7HnjgAVJSUoiKiuL+++8n\nLS2NZcuW0a5dO5u8ET8/P1q3bk1ycjJhYWE0atSI9u3bc+utt9rp0qlTJ6Kjo1myZAlZWVn07t2b\nffv2kZiYyPDhw+nVq5czj69Ehg4dyp133smzzz5LWloabdq0YcOGDTb5FAABAQH07NmTefPmkZeX\nR3BwMNu2bePkyZN2QzWdO3dGRHj++ecZNmwYnp6ePPTQQwwYMIBp06YRGRnJxIkTuXjxIitWrOCW\nW24pc30IZ9p/+eWX2blzJxEREUyaNIk2bdqQkZHB+vXr+frrr/H19WXatGls2LCBqKgoYmJi6Nix\nI1lZWWzZsoW33nqLdu3a0b59e7p27co///lPzp07R0BAAMnJyU4tvhYREUGDBg0YPXo0f//737l2\n7Rpr1qwpSii2YDKZWLJkCUOHDqVDhw6MHz+eoKAgjhw5wtGjR/nggw9s5MeMGcP06dNRSlXaonbV\nTnVMeamKgwpMfe3/Tn95+N2Hyy2v0WhqjiVLlojJZJIePXqUKLNy5Upp3bq1+Pj4yG233SZr1qyx\nm1YqItKsWTN54oknis4dTWe0tBkWFiY+Pj7So0cP2bt3r/Tu3Vvuu+8+G7k5c+ZIaGio+Pr6Spcu\nXWTbtm0yevRoad26tY3cnj17pEuXLuLt7S0mk6loGmxsbKzUq1fPRragoEDi4+MlLCxMvLy8JDQ0\nVGbOnGk3zbZZs2YSFRVl9ywiIiLs9HTE+fPnZcyYMdKwYUNp3LixxMTEyMGDB+2mvp4+fVqioqKk\nUaNG0rhxYxk1apRkZmaKyWSSuXPn2tQ5a9YsCQkJkRtuuMFm+mhKSoq0b99efHx8JDw8XBISEmTF\nihUlTpW1xpn2T548KWPHjpXAwEDx8fGRli1bytNPPy2FhYVFMllZWTJ58mQJCQkRb29vadGihTz+\n+OOSk5NTJJOWlib33nuv+Pj4SHBwsMTFxcmOHTscTn3t1KmTQ7337Nkjd955p/j5+UlISIjExsbK\ntm3bHL5vu3fvlv79+0uDBg2kfv360rFjR1m2bJldnRkZGeLh4SF33HFHqc/MGap76quSSkwoqk6U\nUp2A1NTU1HJvMd/29bZEtopk/oD5VaucRqPRaDSVxK+//kpwcDBz5szhueeeq5Q6rbaY7ywiFZuz\n7QR1JmdDRPQwikaj0WhqHW+++SZA7R1CoQ7lbGTlZZFXkKeDDY1Go9HUCnbu3Mn333/Pyy+/zCOP\nPELTpk1rWqUKU2eCDcu0Vx1saDQajaY2EBcXx4EDB+jdu7fdTKXaRp0JNk5dOAXoBb00Go1GUzuw\nXqa8tlNncjZOXjiJl4cXTfya1LQqGo1Go9HUKepUsNGsYTNMqs6YrNFoNBqNS1BnvrwnL+qZKBqN\nRqPR1AR1Jtg4deGUztfQaDQajaYGqDPBhl5jQ6PRaDSamqFOBBv5hflk5mbqYEOj0Wg0mhrAbYON\n1d+uLtqBMiM3A0GKgo30nHRWf7u6BrXTaDQajabu4LbBRp/QPkVbXlsv6GXZIrtPaJ8a1rD8JCcn\n17QK1YK2073QdroX2k7N9VChYEMp9ZRS6melVJ5S6kulVNdyyP+glLqslPpRKTWm2PV2Sqn3zHVe\nU0r9T0X0ssay5XXMlhgOZhp7zFy7do2YLTEOt8h2ZerKy6/tdC+0ne6FtlNzPTgdbCilHgVeA+KA\njsAhYLtS6qYS5P8GzAFmAu2AF4HFSqlBVmK+QBowDfjFWZ1KwhJwLNq/iPr16jP5o8m1LtDQaDQa\njaa2U5GejSnAMhF5R0SOAE8Cl4GYEuRHm+XfE5F0EXkXWI4RWAAgIgdEZJqIrAP+qIBOJRIaEMr4\nDuPJ/SOXuD5xOtDQaDQajaaacSrYUEp5Ap2BTyxlIiLAx0CPEm7zAq4UK7sCdFNKeTjTfkVIz0ln\nZ/pOPhv3GfGfxxcljWo0Go1Go6kenN2I7SbAAzhbrPws0KaEe7YDjyultojIQaVUF2AC4Gmur3hd\n5cUb4McffyxRIDM3k/jP4onrG0f98/V5JvQZhi0YRlzfOILrB1ew2ernwoULHDx4sKbVqHK0ne6F\nttO90Ha6F1bfTu9qaVBEyn0AtwDXgO7Fyl8B9pVwjzewErgK5AOngHlAIdDEgfzPwP+UQ5dRgOhD\nH/rQhz70oY8KH6OciQMqejjbs/EbRpAQWKw8EDjj6AYRuYLRszHJLPcLMAnIFZFfnWzfmu1ANJCO\n/TCNRqPRaDSakvEGQjG+pVWOU8GGiOQrpVKBfkAKgFJKmc8XlnFvIZBpvmcE8H5FFLaqLwtIup46\nNBqNRqOpw+ytroac7dkAmA+sMgcd+zFmp/gCqwCUUvOAYBEZZz5vBXQDvgIaA88AtwFjLRWaE0/b\nAQqoBzRVSv0VuCQiaRWyTKPRaDQajUvgdLAhIuvMa2rMwhgW+RYYYDUkEgRYb6/qAUwFWmPkbHwK\n9BSRk1YywcA3GONHAM+aj8+Be5zVUaPRaDQajeugzMmWGo1Go9FoNFWC2+6NotFoNBqNxjXQwYZG\no9FoNJoqpVYGG85uBOdKKKXizJvNWR8/FJOZpZTKNG9c979KqZbFrnsppRYrpX5TSuWaN7G7uXot\nsUcp1VsplaKUyjDb9aADmeu2TSnVSCm1Vil1QSmVrZRaqZTyq2r7rNov1U6l1NsOfPxhMRmXtlMp\n9bxSar9S6qJS6qxSapNSqrUDuVrtz/LY6Q7+NLf/pFLqkLn9C0qpvUqpgcVkarU/ze2Xaqe7+LOY\nLtPNdswvVu46/qyOxTwq8wAexVhXYyzQFlgGnAduqmndyql/HPD/gCbAzeajsdX1aWZ7HgBuBzZj\nbFJXz0rmDYz1RfpgbIa3F/jCBWwbiJE4PARjPZYHi12vFNuAj4CDQBegJ3AMSHQhO98GthbzccNi\nMi5tJ/AhMAa4FbgD+MCsr487+bOcdtZ6f5rbH2R+d8OBlsBsjMUWb3UXf5bTTrfwp5UeXYGfMCZZ\nzLcqdyl/VutDqaQH+yWwwOpcAaeB52pat3LqHwccLOV6JjDF6rwBkAcMtzq/Cgy1kmmDsbJrt5q2\nz0qna9h/hK/bNoyPwjWgo5XMAKAACHIRO98GNpZyT2208yazPhFu7k9HdrqdP610yALGu6s/S7DT\nbfwJ+ANHMWZtfoptsOFS/qxVwyiqYhvBuSKtlNEFn6aUSlRKNQNQSv0FY+qwtX0XMdYosdjXBWPK\nsrXMUeAkLvwMKtG2O4FsEfnGqvqPMaZNd68q/StAX3O3/BGl1BKlVGOra52pfXYGmNs+D27tTxs7\nrXArfyqlTMpYXNEX2Ouu/ixup9Uld/HnYuB9EdlpXeiK/qzIol41SUU2gnM1vgQew4hGbwFeBHYp\npW7HeDkEx/YFmf8dCPxhfnFKknFFKsu2IOCc9UURKVRKncd17P8I2ICxz084xl5AHyqlepiD4yBq\nkZ1KKQX8B9gtIpb8IrfzZwl2ghv50/z/zD6MpapzMX7VHlVK9cCN/FmSnebLbuFPcxDVASNoKI7L\n/X3WtmCj1iMi1uvQH1ZK7QdOAMOBIzWjlaYyEZF1VqffK6W+wxgr7YvR1VnbWIKxwm+vmlakinFo\np5v58wjwV6Ah8AjwjlLqrppVqUpwaKeIHHEHfyqlQjAC43tFJL+m9SkPtWoYhQpsBOfqiMgFjISb\nlhg2KEq37wxQTynVoBQZV6SybDuDkdBVhFLKA2MpfJe0X0R+xnh3LZngtcZOpdTrQCTQV0R+sbrk\nVv4sxU47arM/RaRARH4SkW9EZAZwCHgaN/NnKXY6kq2N/uyMkeB6UCmVr5TKx0jyfFop9QdG74RL\n+bNWBRvmCM6yERxgsxFctW0oU5kopfwxXvJM80t/Blv7GmCMjVnsS8VIzrGWaQM0x+g2dEkq0bZ9\nQIBSqqNV9f0w/rC+qir9rwfzr5AbMXY8hlpip/kDPAS4W2y3F3Arf5ZmZwnytdKfJWACvNzJnyVg\nArwcXail/vwYY/ZUB4wenL8CB4BE4K8i8hOu5s/qypqtrANjuOEytlNfs4AmNa1bOfX/N3AX0AJj\nGtH/YkShN5qvP2e2Z7D5ZdoMHMd2utISjPHGvhgR7h5cY+qrn/ml74CRwfwP83mzyrQNY7riAYwp\nX70w8l/WuIKd5muvYvxRtzD/YR4AfgQ8a4udZv2ygd4Yv3Qsh7eVTK33Z1l2uos/ze3PNdvZAmMq\n5DyMj8097uLPsux0J386sLv4bBSX8meNPJRKeKj/B2NucB5G5NWlpnVyQvdkjKm6eRhZv0nAX4rJ\nvIgxbekysB1oWey6F7AIo+svF1gP3OwCtvXB+PgWFjveqkzbMGYMJAIXMD4UKwBfV7ATIyFtG8av\niisY89/foFgw7Op2lmBfITC2st9VV7bTXfxpbn+lWf88sz07MAca7uLPsux0J386sHsnVsGGq/lT\nb8Sm0Wg0Go2mSqlVORsajUaj0WhqHzrY0Gg0Go1GU6XoYEOj0Wg0Gk2VooMNjUaj0Wg0VYoONjQa\njUaj0VQpOtjQaDQajUZTpehgQ6PRaDQaTZWigw2NRqPRaDRVig42NBqNRqPRVCk62NBoNBqNRlOl\n6GBDo9FoNBpNlfL/ATbRK4y9lQkMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4494dee128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "max_idx = all_validation_accuracy.index(max(all_validation_accuracy))\n",
    "print(all_training_size[max_idx], ': ', max(all_validation_accuracy))\n",
    "\n",
    "plt.plot(all_training_size, all_training_accuracy, label='Training data accuracy', marker='x')\n",
    "plt.plot(all_training_size, all_validation_accuracy, label='Validation data accuracy', marker='x')\n",
    "plt.legend(loc='lower right', frameon=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:dsb2017]",
   "language": "python",
   "name": "conda-env-dsb2017-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
